
"""simple docstring"""




from __future__ import annotations

import unittest

from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
   import tensorflow as tf

   from transformers import TFAutoModelForSeqaSeqLM, TFMBartForConditionalGeneration, TFMBartModel

@require_tf
class 						a__		:
   __lowerCAmelCase				=							MBartConfig
   __lowerCAmelCase				=							{}
   __lowerCAmelCase				=							"""gelu"""
   def __init__(     self      ,       _a      ,       _a=13      ,       _a=7      ,       _a=True      ,       _a=False      ,       _a=99      ,       _a=32      ,       _a=2      ,       _a=4      ,       _a=37      ,       _a=0.1      ,       _a=0.1      ,       _a=20      ,       _a=2      ,       _a=1      ,       _a=0      ,       ):
          lowercase       :							str									=				parent
          lowercase       :							Union[str, Any]									=				batch_size
          lowercase       :							Optional[int]									=				seq_length
          lowercase       :							Optional[Any]									=				is_training
          lowercase       :							List[Any]									=				use_labels
          lowercase       :							int									=				vocab_size
          lowercase       :							List[str]									=				hidden_size
          lowercase       :							int									=				num_hidden_layers
          lowercase       :							Dict									=				num_attention_heads
          lowercase       :							Dict									=				intermediate_size
          lowercase       :							List[Any]									=				hidden_dropout_prob
          lowercase       :							Optional[Any]									=				attention_probs_dropout_prob
          lowercase       :							Any									=				max_position_embeddings
          lowercase       :							Tuple									=				eos_token_id
          lowercase       :							Any									=				pad_token_id
          lowercase       :							Dict									=				bos_token_id
   def      __magic_name__     (     self							):
          lowercase       :							List[str]									=				ids_tensor([self.batch_size, self.seq_length - 1]      ,       self.vocab_size							)
          lowercase       :							Union[str, Any]									=				tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size							)      ,       1							)
          lowercase       :							Dict									=				tf.concat([input_ids, eos_tensor]      ,       axis=1							)

          lowercase       :							Tuple									=				ids_tensor([self.batch_size, self.seq_length]      ,       self.vocab_size							)

          lowercase       :							Optional[int]									=				self.config_cls(
              vocab_size=self.vocab_size      ,       d_model=self.hidden_size      ,       encoder_layers=self.num_hidden_layers      ,       decoder_layers=self.num_hidden_layers      ,       encoder_attention_heads=self.num_attention_heads      ,       decoder_attention_heads=self.num_attention_heads      ,       encoder_ffn_dim=self.intermediate_size      ,       decoder_ffn_dim=self.intermediate_size      ,       dropout=self.hidden_dropout_prob      ,       attention_dropout=self.attention_probs_dropout_prob      ,       max_position_embeddings=self.max_position_embeddings      ,       eos_token_ids=[2]      ,       bos_token_id=self.bos_token_id      ,       pad_token_id=self.pad_token_id      ,       decoder_start_token_id=self.pad_token_id      ,       **self.config_updates      ,       )
          lowercase       :							Any									=				prepare_mbart_inputs_dict(snake_case_      ,       snake_case_      ,       snake_case_							)
          return config, inputs_dict

   def      __magic_name__     (     self      ,       _a      ,       _a							):
          lowercase       :							List[str]									=				TFMBartModel(config=snake_case_							).get_decoder()
          lowercase       :							List[Any]									=				inputs_dict["""input_ids"""]

          lowercase       :							Optional[Any]									=				input_ids[:1, :]
          lowercase       :							List[str]									=				inputs_dict["""attention_mask"""][:1, :]
          lowercase       :							Optional[int]									=				inputs_dict["""head_mask"""]
          lowercase       :							str									=				1

          # first forward pass
          lowercase       :							Tuple									=				model(snake_case_      ,       attention_mask=snake_case_      ,       head_mask=snake_case_      ,       use_cache=snake_case_							)

          lowercase       :							str									=				outputs.to_tuple()
          lowercase       :							List[Any]									=				past_key_values[1]


def     __magic_name__      ( __snake_case							:			int	,					__snake_case							:			Optional[Any]	,					__snake_case							:			List[str]	,					__snake_case							:			Dict=None	,					__snake_case							:			Optional[int]=None	,					__snake_case							:			Optional[int]=None	,					__snake_case							:			str=None	,					__snake_case							:			Tuple=None	,					)					->							Union[str, Any]:
       if attention_mask is None:
              lowercase       :							str									=				tf.cast(tf.math.not_equal(__snake_case	,					config.pad_token_id    )	,					tf.inta    )
       if decoder_attention_mask is None:
              lowercase       :							int									=				tf.concat(
                  [
                      tf.ones(decoder_input_ids[:, :1].shape	,					dtype=tf.inta    ),
                      tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]	,					config.pad_token_id    )	,					tf.inta    ),
                  ]	,					axis=-1	,					)
       if head_mask is None:
              lowercase       :							Optional[Any]									=				tf.ones((config.encoder_layers, config.encoder_attention_heads)    )
       if decoder_head_mask is None:
              lowercase       :							str									=				tf.ones((config.decoder_layers, config.decoder_attention_heads)    )
       if cross_attn_head_mask is None:
              lowercase       :							str									=				tf.ones((config.decoder_layers, config.decoder_attention_heads)    )
       return {
           "input_ids": input_ids,
           "decoder_input_ids": decoder_input_ids,
           "attention_mask": attention_mask,
           "decoder_attention_mask": decoder_attention_mask,
           "head_mask": head_mask,
           "decoder_head_mask": decoder_head_mask,
           "cross_attn_head_mask": cross_attn_head_mask,
       }

@require_tf
class 						a__		( _A,     _A,     unittest.TestCase		):
   __lowerCAmelCase				=							(TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
   __lowerCAmelCase				=							(TFMBartForConditionalGeneration,) if is_tf_available() else ()
   __lowerCAmelCase				=							(
       {
           """conversational""": TFMBartForConditionalGeneration,
           """feature-extraction""": TFMBartModel,
           """summarization""": TFMBartForConditionalGeneration,
           """text2text-generation""": TFMBartForConditionalGeneration,
           """translation""": TFMBartForConditionalGeneration,
       }
       if is_tf_available()
       else {}
   )
   __lowerCAmelCase				=							True
   __lowerCAmelCase				=							False
   __lowerCAmelCase				=							False
   def      __magic_name__     (     self      ,       _a      ,       _a      ,       _a      ,       _a      ,       _a							):
          if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
                 # Exception encountered when calling layer '...'
                 return True

          return False
   def      __magic_name__     (     self							):
          lowercase       :							Any									=				TFMBartModelTester(self							)
          lowercase       :							Optional[int]									=				ConfigTester(self      ,       config_class=snake_case_							)
   def      __magic_name__     (     self							):
          self.config_tester.run_common_tests()

   def      __magic_name__     (     self							):
          lowercase       :							str									=				self.model_tester.prepare_config_and_inputs_for_common()
          self.model_tester.check_decoder_model_past_large_inputs(*snake_case_							)


@require_sentencepiece
@require_tokenizers
@require_tf
class 						a__		( unittest.TestCase		):
   __lowerCAmelCase				=							[
       """ UN Chief Says There Is No Military Solution in Syria""",
   ]
   __lowerCAmelCase				=							[
       """Şeful ONU declară că nu există o soluţie militară în Siria""",
   ]
   __lowerCAmelCase				=							"""facebook/mbart-large-en-ro"""
   @cached_property
   def      __magic_name__     (     self							):
          return AutoTokenizer.from_pretrained(self.model_name							)
   @cached_property
   def      __magic_name__     (     self							):
          lowercase       :							int									=				TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name							)
          return model
   def      __magic_name__     (     self      ,       **_a							):
          lowercase       :							str									=				self.translate_src_text(**snake_case_							)
          self.assertListEqual(self.expected_text      ,       snake_case_							)
   def      __magic_name__     (     self      ,       **_a							):
          lowercase       :							Optional[Any]									=				self.tokenizer(self.src_text      ,       **snake_case_      ,       return_tensors="tf"							)
          lowercase       :							Any									=				self.model.generate(
              model_inputs.input_ids      ,       attention_mask=model_inputs.attention_mask      ,       num_beams=2							)
          lowercase       :							Union[str, Any]									=				self.tokenizer.batch_decode(snake_case_      ,       skip_special_tokens=snake_case_							)
          return generated_words

   @slow
   def      __magic_name__     (     self							):
          self._assert_generated_batch_equal_expected()




import heapq
import sys

import numpy as np

lowerCamelCase_    :			Optional[Any]    =	tuple[int, int]

class 							_UpperCamelCase					:






			'''simple docstring'''

			def __init__(							self			:							Any   ):
							UpperCamelCase_:				Union[str, Any]						=    []
							UpperCamelCase_:				str						=    set()

			def    lowerCAmelCase__						(							self			:							List[Any]   ):
							if not self.empty():
											return self.elements[0][0]
							else:
											return float("""inf"""   )

			def    lowerCAmelCase__						(							self			:							List[Any]   ):
							return len(self.elements   ) == 0

			def    lowerCAmelCase__						(							self			:							Union[str, Any]    ,						snake_case_			:							int    ,						snake_case_			:							Dict   ):
							if item not in self.set:
											heapq.heappush(self.elements    ,						(priority, item)   )
											self.set.add(snake_case_   )
							else:
											# update
											# print("update", item)
											UpperCamelCase_:				Any						=    []
											((UpperCamelCase_), (UpperCamelCase_)):				Any						=    heapq.heappop(self.elements   )
											while x != item:
															temp.append((pri, x)   )
															((UpperCamelCase_), (UpperCamelCase_)):				str						=    heapq.heappop(self.elements   )
											temp.append((priority, item)   )
											for pro, xxx in temp:
															heapq.heappush(self.elements    ,						(pro, xxx)   )

			def    lowerCAmelCase__						(							self			:							Union[str, Any]    ,						snake_case_			:							Union[str, Any]   ):
							if item in self.set:
											self.set.remove(snake_case_   )
											UpperCamelCase_:				Dict						=    []
											((UpperCamelCase_), (UpperCamelCase_)):				Tuple						=    heapq.heappop(self.elements   )
											while x != item:
															temp.append((pro, x)   )
															((UpperCamelCase_), (UpperCamelCase_)):				Any						=    heapq.heappop(self.elements   )
											for prito, yyy in temp:
															heapq.heappush(self.elements    ,						(prito, yyy)   )

			def    lowerCAmelCase__						(							self			:							Optional[int]   ):
							return self.elements[0][1]




			def    lowerCAmelCase__						(							self			:							List[str]   ):
							((UpperCamelCase_), (UpperCamelCase_)):				Any						=    heapq.heappop(self.elements   )
							self.set.remove(snake_case_   )
							return (priority, item)







def 							A__       (    lowerCamelCase  ,     lowerCamelCase    )	->						str:
				# euclidean distance
				UpperCamelCase_:				Any						=    np.array(lowerCamelCase    )
				UpperCamelCase_:				Tuple						=    np.array(lowerCamelCase    )
				return np.linalg.norm(a - b    )







def 							A__       (    lowerCamelCase  ,     lowerCamelCase    )	->						List[str]:
				# integer division by time variable
				return consistent_heuristic(lowerCamelCase  ,     lowerCamelCase    ) // t







def 							A__       (    lowerCamelCase  ,     lowerCamelCase    )	->						Optional[Any]:
				# manhattan distance
				return abs(p[0] - goal[0]    ) + abs(p[1] - goal[1]    )







def 							A__       (    lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase    )	->						Optional[int]:
				UpperCamelCase_:				Dict						=    g_function[start] + Wa * heuristics[i](lowerCamelCase  ,     lowerCamelCase    )
				return ans







def 							A__       (    lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase    )	->						List[str]:
				UpperCamelCase_:				Any						=    np.chararray((n, n)    )
				for i in range(lowerCamelCase    ):
								for j in range(lowerCamelCase    ):
												UpperCamelCase_:				List[str]						=    """*"""

				for i in range(lowerCamelCase    ):
								for j in range(lowerCamelCase    ):
												if (j, (n - 1) - i) in blocks:
																UpperCamelCase_:				List[str]						=    """#"""

				UpperCamelCase_:				int						=    """-"""
				UpperCamelCase_:				Tuple						=    back_pointer[goal]
				while x != start:
								((UpperCamelCase_), (UpperCamelCase_)):				List[str]						=    x
								# print(x)
								UpperCamelCase_:				List[Any]						=    """-"""
								UpperCamelCase_:				List[str]						=    back_pointer[x]
				UpperCamelCase_:				Optional[Any]						=    """-"""

				for i in range(lowerCamelCase    ):
								for j in range(lowerCamelCase    ):
												if (i, j) == (0, n - 1):
																print(grid[i][j]  ,     end=""" """    )
																print("""<-- End position"""  ,     end=""" """    )
												else:
																print(grid[i][j]  ,     end=""" """    )
								print()
				print("""^"""    )
				print("""Start position"""    )
				print()
				print("""# is an obstacle"""    )
				print("""- is the path taken by algorithm"""    )
				print("""PATH TAKEN BY THE ALGORITHM IS:-"""    )
				UpperCamelCase_:				Optional[Any]						=    back_pointer[goal]
				while x != start:
								print(lowerCamelCase  ,     end=""" """    )
								UpperCamelCase_:				Any						=    back_pointer[x]
				print(lowerCamelCase    )
				sys.exit()







def 							A__       (    lowerCamelCase    )	->						Optional[Any]:
				if p[0] < 0 or p[0] > n - 1:
								return False
				if p[1] < 0 or p[1] > n - 1:
								return False
				return True







def 							A__       (    lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     )	->						Any:
				for itera in range(lowerCamelCase    ):
								open_list[itera].remove_element(lowerCamelCase    )
				# print("s", s)
				# print("j", j)
				((UpperCamelCase_), (UpperCamelCase_)):				Optional[Any]						=    s
				UpperCamelCase_:				Dict						=    (x - 1, y)
				UpperCamelCase_:				Dict						=    (x + 1, y)
				UpperCamelCase_:				Any						=    (x, y + 1)
				UpperCamelCase_:				Union[str, Any]						=    (x, y - 1)

				for neighbours in [left, right, up, down]:
								if neighbours not in blocks:
												if valid(lowerCamelCase    ) and neighbours not in visited:
																# print("neighbour", neighbours)
																visited.add(lowerCamelCase    )
																UpperCamelCase_:				Dict						=    -1
																UpperCamelCase_:				Any						=    float("""inf"""    )

												if valid(lowerCamelCase    ) and g_function[neighbours] > g_function[s] + 1:
																UpperCamelCase_:				int						=    g_function[s] + 1
																UpperCamelCase_:				str						=    s
																if neighbours not in close_list_anchor:
																				open_list[0].put(lowerCamelCase  ,     key(lowerCamelCase  ,     0  ,     lowerCamelCase  ,     lowerCamelCase    )    )
																				if neighbours not in close_list_inad:
																								for var in range(1  ,     lowerCamelCase    ):
																												if key(lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase    ) <= Wa * key(
																												    lowerCamelCase  ,     0  ,     lowerCamelCase  ,     lowerCamelCase    ):
																																open_list[j].put(
																																    lowerCamelCase  ,     key(lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase    )    )







def 							A__       (    )	->						int:
				UpperCamelCase_:				List[Any]						=    []
				for x in range(1  ,     5    ):
								for y in range(1  ,     6    ):
												some_list.append((x, y)    )

				for x in range(15  ,     20    ):
								some_list.append((x, 17)    )

				for x in range(10  ,     19    ):
								for y in range(1  ,     15    ):
												some_list.append((x, y)    )

    # L block
				for x in range(1  ,     4    ):
								for y in range(12  ,     19    ):
												some_list.append((x, y)    )
				for x in range(3  ,     13    ):
								for y in range(16  ,     19    ):
												some_list.append((x, y)    )
				return some_list


lowerCamelCase_    :			int    =	{0: consistent_heuristic, 1: heuristic_a, 2: heuristic_a}

lowerCamelCase_    :			List[str]    =	[
    (0, 1),
    (1, 1),
    (2, 1),
    (3, 1),
    (4, 1),
    (5, 1),
    (6, 1),
    (7, 1),
    (8, 1),
    (9, 1),
    (10, 1),
    (11, 1),
    (12, 1),
    (13, 1),
    (14, 1),
    (15, 1),
    (16, 1),
    (17, 1),
    (18, 1),
    (19, 1),
]
lowerCamelCase_    :			int    =	make_common_ground()


lowerCamelCase_    :			List[Any]    =	blocks_blk
# hyper parameters
lowerCamelCase_    :			Dict    =	1
lowerCamelCase_    :			Union[str, Any]    =	1
lowerCamelCase_    :			str    =	20
lowerCamelCase_    :			Any    =	3  # one consistent and two other inconsistent

# start and end destination
lowerCamelCase_    :			Optional[Any]    =	(0, 0)
lowerCamelCase_    :			int    =	(n - 1, n - 1)

lowerCamelCase_    :			Optional[Any]    =	1







def 							A__       (    lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase    )	->						Optional[int]:
				UpperCamelCase_:				List[str]						=    {start: 0, goal: float("""inf"""    )}
				UpperCamelCase_:				Dict						=    {start: -1, goal: -1}
				UpperCamelCase_:				Any						=    []
				UpperCamelCase_:				List[str]						=    set()

				for i in range(lowerCamelCase    ):
								open_list.append(PriorityQueue()    )
								open_list[i].put(lowerCamelCase  ,     key(lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase    )    )

				UpperCamelCase_:				list[int]						=    []
				UpperCamelCase_:				list[int]						=    []
				while open_list[0].minkey() < float("""inf"""    ):
								for i in range(1  ,     lowerCamelCase    ):
												# print(open_list[0].minkey(), open_list[i].minkey())
												if open_list[i].minkey() <= Wa * open_list[0].minkey():
																global t
																t += 1
																if g_function[goal] <= open_list[i].minkey():
																				if g_function[goal] < float("""inf"""    ):
																								do_something(lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase    )
																else:
																				UpperCamelCase_, UpperCamelCase_:				Any						=    open_list[i].top_show()
																				visited.add(lowerCamelCase    )
																				expand_state(
																				    lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     )
																				close_list_inad.append(lowerCamelCase    )
												else:
																if g_function[goal] <= open_list[0].minkey():
																				if g_function[goal] < float("""inf"""    ):
																								do_something(lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase    )
																else:
																				UpperCamelCase_:				Dict						=    open_list[0].top_show()
																				visited.add(lowerCamelCase    )
																				expand_state(
																				    lowerCamelCase  ,     0  ,     lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     )
																				close_list_anchor.append(lowerCamelCase    )
				print("""No path found to goal"""    )
				print()
				for i in range(n - 1  ,     -1  ,     -1    ):
								for j in range(lowerCamelCase    ):
												if (j, i) in blocks:
																print("""#"""  ,     end=""" """    )
												elif (j, i) in back_pointer:
																if (j, i) == (n - 1, n - 1):
																				print("""*"""  ,     end=""" """    )
																else:
																				print("""-"""  ,     end=""" """    )
												else:
																print("""*"""  ,     end=""" """    )
												if (j, i) == (n - 1, n - 1):
																print("""<-- End position"""  ,     end=""" """    )
								print()
				print("""^"""    )
				print("""Start position"""    )
				print()
				print("""# is an obstacle"""    )
				print("""- is the path taken by algorithm"""    )


if __name__ == "__main__":
							multi_a_star(start, goal, n_heuristic)






